SNP-based mixed model association of growth- and yield-related traits in popcorn

Abstract

The identification of the genes responsible for complex traits is highly promising to accelerate crop breeding, but such information is still limited for popcorn. Thus, in the present study, a mixed linear model-based association analysis (MLMA) was applied for six important popcorn traits: plant and ear height, 100-grain weight, popping expansion, grain yield and expanded popcorn volume per hectare. To this end, 196 plants of the open-pollinated popcorn population UENF-14 were sampled, selfed (S 1 ), and then genotyped with a panel of 10,507 single nucleotide polymorphisms (SNPs) markers distributed throughout the genome. The six traits were studied under two environments [Campos dos Goytacazes-RJ (ENV1) and Itaocara-RJ (ENV2)] in an incomplete block design. Based on the phenotypic data of the S 1 progenies and on the genetic characteristics of the parents, the MLMA was performed. Thereafter, genes annotated in the MaizeGDB platform were screened for potential linkage disequilibrium with the SNPs associated to the six evaluated traits. Overall, seven and eight genes were identified as associated with the traits in ENV1 and ENV2, respectively, and proteins encoded by these genes were evaluated for their function. The results obtained here contribute to increase knowledge on the genetic architecture of the six evaluated traits and might be used for marker-assisted selection in breeding programs.

A Deep Learning Approach to Population Structure Inference in Inbred Lines of Maize

“…Analysis of population genetic structure is a major area of interest within the field of genetics and bioinformatics, which is a common practice in genome-wide studies, including association mapping, ecology, and evolution studies in crop species such as maize (Li et al, 2019;Mafra et al, 2019;Maldonado et al, 2019;Wang et al, 2019). The present study proposed an MLbased analysis of population structure and individual assignment usually performed in several data-intensive biological fields.…”

Genomic Predictions Using Low-Density SNP Markers, Pedigree and GWAS Information: A Case Study with the Non-Model Species Eucalyptus cladocalyx

“…In this sense, genotype-phenotype studies for quantitative traits at the genome level usually require high-density genetic marker panels, i.e., a large number of markers throughout the genome and large population sizes to obtain sufficient power and prediction resolution [1,2]. The development of several genotyping platforms through high-density single nucleotide polymorphism (SNP) arrays, such as genotyping-by-sequencing (GBS) or SNP chips, has enabled the identification of quantitative trait loci (QTL) for different target traits in various plant species [3][4][5][6]. Silva-Junior et al [7], for instance, developed a genome-wide SNP chip for multiple species of Eucalyptus, which has been effective for genomic studies in a wide variety of economically important eucalypt species and their hybrids, including Eucalyptus grandis, Eucalyptus urophylla, Eucalyptus nitens and Eucalyptus globulus [8][9][10][11][12].…”

Regional Heritability Mapping of Quantitative Trait Loci Controlling Traits Related to Growth and Productivity in Popcorn (Zea mays L.)

“…A successful tool to explain the genetic basis of complex traits in association studies, which allow the identification of quantitative trait loci (QTLs) based on the significant associations between genotypic markers and phenotypic data [ 8 ]. The identification of QTLs related to PE and GY has been reported in several studies, for example, simple sequence repeat (SSR) and single-nucleotide polymorphism (SNP) markers [ 5 , 8 , 9 , 10 , 11 , 12 ]. Thakur et al [ 10 ] identified three QTLs associated (using SSR markers) with the popping volume, which covers 78% of total phenotypic variance.…”

“…The SNP has been widely used in association studies for the detection of a large number of QTLs and candidate genes involved in the yield and growth [ 8 , 14 , 15 ]. Despite their wide usage, association studies have been criticized, since according to certain studies, they are inefficient in detecting the total genetic variation of complex traits [ 16 , 17 ].…”